Determining the appropriate model complexity for patient-specific advice on mechanical ventilation

Abstract

Mathematical physiological models can be applied in medical decision support systems. To do so requires consideration of the necessary model complexity. Models that simulate changes in the individual patient are required, meaning that models should have a complexity where parameters can be uniquely identified at the bedside from clinical data and where the models adequately represent the individual patient's (patho)physiology. This paper describes the models included in a system for providing decision support for mechanical ventilation. Models of pulmonary gas exchange, respiratory mechanics, acid-base, and respiratory control are described. The parameters of these models are presented along with the necessary clinical data required for their estimation and the parameter estimation process. In doing so, the paper highlights the need for simple, minimal models for application at the bedside, directed toward well-defined clinical problems.

Simulation-Based Evaluation of Mechanical Ventilators

“…AI is being applied to mechanical ventilation. 31,32 While promising, these approaches have limited clinical acceptance and are limited to data gathered from one patient at a time. More successful AI, like IBM's Watson or Google's Alpha Zero, use deep learning provided by artificial neural networks that digest vast amounts of dynamic big data.…”

Intensive Care Weaning (iCareWean) protocol on weaning from mechanical ventilation: a single-blinded multicentre randomised control trial comparing an open-loop decision support system and routine care, in the general intensive care unit

“…At the core, functionality of the BEACON Caresystem is a set of physiological models including models of pulmonary gas exchange, acid-base chemistry, lung mechanics and respiratory drive. 9 The BEACON Caresystem tunes these models to the individual patient so that they accurately describe the current physiological measurements. Once the models are tuned, they are then used by the system to simulate the effects of a change in ventilator settings.…”

Reliability of, and Agreement Between, two Breath‐by‐Breath Indirect Calorimeters at Varying Levels of Inspiratory Oxygen

“…A new bedside system for decision support of mechanical ventilation, Beacon Caresystem (device 2; Mermaid Care A/S, Noerresundby, Denmark), includes IC functionality. In contrast to other IC systems, device 2 performs calculations based on inspired and expired volume but without the Haldane transformation, potentially allowing it to measure across the full FiO 2 range.…”

“…16 This may limit IC use in the most critically ill ICU patients, for whom high levels of FiO 2 are common and who are precisely the patient population for whom malnutrition and poor outcome are correlated. [1][2][3] A new bedside system for decision support of mechanical ventilation, Beacon Caresystem (device 2; Mermaid Care A/S, Noerresundby, Denmark), 17 includes IC functionality. In contrast to other IC systems, device 2 performs calculations based on inspired and expired volume but without the Haldane transformation, potentially allowing it to measure across the full FiO 2 range.…”